In the course of operations, businesses decide from whom they will obtain the various goods and services they require. In manufacturing, raw materials for a product that are to be processed or assembled must be replaced when exhausted if additional products are to be manufactured. Similarly, a service business often consumes supplies in the process of delivering services to its customers that must be replaced if the services are to continue. Supplies may be tangible goods, for example iron and coke used to make steel, or they may be intangible services, for example collection services for collecting delinquent payments. Therefore, throughout this specification, the term “item” is used to refer to both goods and services.
Currently, a buyer seeking to acquire one or more items may create a reverse auction by distributing a “request-for-quotation” (hereinafter “RFQ”) to prospective suppliers. The RFQ lists at least the items the buyer desires to purchase. The RFQ may also contain additional information concerning the proposed transaction, such as the minimum or maximum quantities of a particular desired item or items, delivery dates for particular items, or a required quality level for an item. The RFQ can thus be viewed as a collection of constraints imposed by the buyer that describe a proposed transaction. In response to the RFQ, the prospective suppliers submit bids that are offers to enter into a contract with the buyer on specified terms. These bids typically include prices and may include other additional proposed terms. Accordingly, the response can be viewed as a collection of constraints imposed by the prospective supplier on the proposed transaction.
To the extent that the constraints imposed by the buyer and the constraints imposed by a particular supplier are both satisfied, a transaction between the buyer and the particular supplier is possible. In a typical auction, there will be numerous suppliers for which this is true. The buyer must then choose which of those suppliers are to be awarded the bid. The optimal combination of suppliers, together with the list of items to be ordered from each supplier, is referred to as an “optimal award schedule.”
Where price is the buyer's sole concern, and all bids can yield a unit price-per-item, the process of determining an optimal award schedule is simple: one selects the supplier offering the lowest price-per-item. If the buyer requires additional quantities of that item when that supplier's supply of the item is exhausted, the buyer then selects the supplier having the next lowest price-per-item. This process continues until the buyer's constraint on the quantity of the item has been met.
However, modern business-to-business transactions are far from that simple. For example, a supplier's price for an item can depend on the quantity of the item purchased. Or, the supplier may bid one price for a bundle of disparate items, in which case it is unclear how the buyer should allocate this price among the items. In addition, other less clearly quantifiable factors must often be considered. For example, the quality of goods, the reputation of the supplier for reliability, or the supplier's solvency may require evaluation. The buyer may also have internally generated policies, or business rules, that further constrain the choice of which suppliers can be awarded which bids.
The complexity of compiling a quantitatively justifiable schedule of optimal awards given all the foregoing constraints is daunting even when the choice is limited to a few suppliers bidding on a limited number of items. As a result, decision-makers often rely on what is euphemistically termed “heuristic reasoning” when awarding bids to suppliers. That decisions of such importance are based on what amounts to educated guesswork is alarming, particularly in an era in which computational tools are so widely available. Therefore, a need exists for computational tools for the compilation of a quantitatively justifiable schedule of optimal awards given specified constraints.